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The defects in wind turbines not only affect energy generation efficiency but can also lead to significant damage if not repaired promptly. To address the challenges of low detection efficiency and high costs in the real-world industrial scenario of wind turbine defect detection, we have designed a lightweight detection model. First, this study introduces Receptive-Field Attention Convolution(RFAConv) and develops the Cross Stage Partial with 2 convolutions and feature fusion-Receptive-Field Attention Convolution(C2f-RFAConv) module, integrating it into the backbone network. This approach allows the model to focus on spatial features while accurately capturing local information in each region through its receptive field, significantly enhancing its feature extraction capabilities. Additionally, we incorporate Group Shuffle Convolution(GSConv) in the neck network to ensure that the model remains lightweight while maintaining a high level of accuracy. In the design of the detection head, we leverage the low redundant computation capability of Spatial and Channel reconstruction Convolution(SCConv), along with its ability to promote the learning of representative features, to develop a detection head-SCConv Head-that integrates classification and detection with low computational cost and parameters. All experimental results are reported as the average of no fewer than three independent runs to ensure the stability and reliability of the results. Experimental results show that, compared to the original You Only Look Once version 8 nano(YOLOv8n), our model reduces its size by 1.16 MB and decreases the floating-point operations by 3.5 G while improving the mean Average Precision (mAP) by 3.7%. These results demonstrate the effectiveness of our model in achieving lightweight performance.

The quench dynamics of a system involving two competing orders is investigated using a Ginzburg-Landau theory with relaxational dynamics. We consider the scenario where a pump rapidly heats the system to a high temperature, after which the system cools down to its equilibrium temperature. We study the evolution of the order parameter amplitude and fluctuations in the resulting time-dependent free-energy landscape. Exponentially growing thermal fluctuations dominate the dynamics. The system typically evolves into the phase associated with the faster-relaxing order parameter, even if it is not the global free-energy minimum. This theory offers a natural explanation for the widespread experimental observation that metastable states may be induced by laser-induced collapse of a dominant equilibrium order parameter.

In coal production, the raw coal contains a large amount of gangue, which affects the quality of coal and pollutes the environment. Separating coal and gangue can improve coal quality, save energy, and reduce consumption and make rational use of resources. The separated gangue can also be reused. Robots with computer vision technology have become current research hotspots due to simple equipment, are efficient, and create no pollution to the environment. However, the difficulty in identifying coal and gangue is that the difference between coal and gangue is small, and the background and prospects are similar. In addition, due to the irregular shape of gangue, real-time grasping requirements make robot control difficult. This paper presents a coal and gangue separating robot system based on computer vision, proposes a convolutional neural network to extract the classification and location information, and designs a robot multi-objective motion planning algorithm. Through simulation and experimental verification, the accuracy of coal gangue identification reaches 98% under the condition of ensuring real-time performance. The average separating rate reaches 75% on low-, medium-, and high-speed moving conveyor belts, which meets the needs of actual projects. This method has important guiding significance in detection and separation of objects in complex scenes.

Conductive polymer hydrogels (CPHs) are gaining considerable attention in developing wearable electronics due to their unique combination of high conductivity and softness. However, in the absence of interactions, the incompatibility between hydrophobic conductive polymers (CPs) and hydrophilic polymer networks gives rise to inadequate bonding between CPs and hydrogel matrices, thereby significantly impairing the mechanical and electrical properties of CPHs and constraining their utility in wearable electronic sensors. Therefore, to endow CPHs with good performance, it is necessary to ensure a stable and robust combination between the hydrogel network and CPs. Encouragingly, recent research has demonstrated that incorporating supramolecular interactions into CPHs enhances the polymer network interaction, improving overall CPH performance. However, a comprehensive review focusing on supramolecular CPH (SCPH) for wearable sensing applications is currently lacking. This review provides a summary of the typical supramolecular strategies employed in the development of high‐performance CPHs and elucidates the properties of SCPHs that are closely associated with wearable sensors. Moreover, the review discusses the fabrication methods and classification of SCPH sensors, while also exploring the latest application scenarios for SCPH wearable sensors. Finally, it discusses the challenges of SCPH sensors and offers suggestions for future advancements. This review summarizes typical supramolecular strategies for developing high‐performance conducting polymer hydrogels (CPHs) and elucidates their properties relevant to wearable sensors. Additionally, it discusses the fabrication methods and classification of supramolecular CPH sensors and their latest applications in wearable devices. Finally, the review addresses these sensors’ challenges and provides suggestions for future developments.

For accelerometers targeted in inertial navigation field, the DC bias error is the most destructive system error, affecting the final precision of long-term dead reckoning. This paper proposes a novel self-test and self-calibration technique for canceling out the DC bias error of the digital closed-loop accelerometers. The self-test of system DC bias is realized by injecting a 1-Bit ΣΔ modulated digital excitation and measuring the second-order harmonic distortion. As illustrated, the second-order harmonic distortion is related to the servo position deviation of the MEMS sensing element, which is one of the main causes of system DC bias error. The automatic capacitance compensation is carried out based on the amplitude and phase information of the detected second-order harmonic distortion, which can dynamically calibrate out the DC bias error. Test results show that there exists a near-linearity relationship between the system DC bias error and the second-order harmonic distortion, which is consistent with the proposed theoretical deduction. Based on the proposed method, the system DC bias error is effectively reduced from 150 to 4 mg, and unaffected by external acceleration bias.

With the continuous development of the robotics industry, using a single wireless system to charge different types of robots has become a critical issue that urgently needs to be addressed. To solve this problem, in the present work, we propose a single-switch inverter module wireless charging system based on parallel module number frequency modulation to achieve the expected variable voltage output by adjusting the operating frequency and the number of parallel modules, thereby enhancing the interoperability between devices. To meet the charging requirements of lithium batteries, which require constant current (CC) first and constant voltage (CV) thereafter, we first discuss how to implement CC and CV charging modes, then demonstrate that the proposed system can provide the required CC and CV output under various load conditions. Subsequently, a simplified equivalent circuit model to achieve this wireless charging system is proposed and an exact expression for its equivalent input voltage source is provided. Subsequently, based on the analysis of the amplitude–frequency characteristics of voltage gain under the CV mode, we propose the relevant method and program to realize this variable output system, and specifically build a prototype system based on a three-module parallel configuration. Experimental results show that the present prototype system can indeed provide the constant current (CC) and constant voltage (CV) outputs required for lithium battery charging, and the expected variable voltage output achieved by frequency modulation (FM) is verified. Its maximum efficiency can approach 91.3%. Compared with other wireless charging systems with single-switch inverters, this prototype experimental system possesses significant advantages in completing the full charging process of lithium batteries, maintaining stable voltage output during the constant voltage phase, and enabling flexible multi-voltage output.

Flexible and ultrathin pressure sensors have received significant attention in applications of health monitoring, artificial skin, human–machine interfaces, soft robotics, etc. Bacterial cellulose (BC) is a renewable resource widely used in tissue engineering and flexible pressure sensors owing to its excellent biocompatibility. In this study, after simple mixing (mass ratio 1:1) and freeze-drying of BC and silver nanowires (AgNWs) suspensions, cylindrical BC-AgNWs aerogels (ф = 14 mm, h = 1.6 mm) were obtained. The BC-AgNWs aerogels showed a porous and fluffy structure with an ultralight mass of 3.2 mg. After compression, ultrathin (30 μm) BC-AgNWs films could be easily fabricated. The BC-AgNWs films had excellent mechanical properties and an ultrahigh sensitivity of 47.1 mA kPa−1 V−1. Ultrathin (~ 40 μm) flexible pressure sensors were assembled by encapsulating the BC-AgNWs films between 1-μm-thick polyethylene terephthalate (PET) films. The assembled PET/BC-AgNWs film/PET pressure sensors had a detection limit of 400 Pa and a fast response time of 220 ms. Moreover, the sensors were used to detect human motions such as finger bending, wrist bending, elbow bending, finger pressing, swallowing, and squatting, showing good application prospects in the field of health monitoring.

A general relation is derived between the linear and second-order nonlinear ac conductivities of an electron system in the hydrodynamic regime of frequencies below the interparticle scattering rate. The magnitude and tensorial structure of the hydrodynamic nonlinear conductivity are shown to differ from their counterparts in the more familiar kinetic regime of higher frequencies. Due to universality of the hydrodynamic equations, the obtained formulas are valid for systems with an arbitrary Dirac-like dispersion, ranging from solid-state electron gases to free-space plasmas, either massive or massless, at any temperature, chemical potential, or space dimension. Predictions for photon drag and second-harmonic generation in graphene are presented as one application of this theory.

Background The global prevalence of type 2 diabetes mellitus (T2DM) is rising, significantly increasing the risk of cognitive impairment and dementia. Although exercise improves cognitive function in T2DM, few studies have compared different exercise modalities. This network meta-analysis assessed their effects on global cognitive function in patients with T2DM. Methods This study systematically searched PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure (CNKI), and Wanfang databases from their inception to October 10, 2025, and included randomized controlled trials (RCTs) evaluating the effects of exercise interventions on global cognitive function in patients with T2DM. Standardized mean differences (SMDs) and 95% confidence intervals (CIs) were pooled using a random-effects model, and treatment rankings were estimated using surface under the cumulative ranking curve (SUCRA) values. Subgroup analyses were performed according to cognitive assessment tools, intervention duration, and training frequency. Results Twenty-one RCTs involving 2,118 participants were included. Compared with usual care, multimodal exercise (ME, 7 trials, n  = 423, SMD = 1.04, 95% CI: 0.49–1.59), aerobic exercise (AE, 9 trials, n  = 399, SMD = 0.85, 95% CI༚0.38–1.33), and mind–body exercise (MBE, 4 trials, n  = 202, SMD = 0.93, 95% CI༚0.25–1.62) significantly improved global cognitive function, while resistance exercise (RE, 2 trials, n  = 77, SMD = 0.46, 95% CI༚–0.57–1.48) showed no significant effect. SUCRA rankings indicated the highest efficacy for ME (78.1%), followed by MBE (69.0%), AE (61.5%), and RE (36.6%). Subgroup analyses showed that ME was most effective when cognition was assessed with MoCA and in long-term interventions (> 3 months), whereas MBE and AE were more effective with MMSE and in short-term interventions (≤ 3 months). At exercise frequencies ≤ 3 sessions per week, ME, AE, and MBE were effective, while at higher frequencies only ME remained effective. Conclusion This study indicates that ME is the most effective intervention for improving global cognitive function in patients with T2DM, while MBE and AE also provide benefits. In clinical practice, exercise interventions should be tailored to individual patient characteristics to optimize cognitive outcomes.

SignificanceThe emergence of superconductivity in high-temperature cuprates arises out of a rich landscape of competing order. For example, stripe order can hoard the electrons needed to form Cooper pairs and establish superconductivity. Intriguingly, the complex interactions of such intertwined orders can be manipulated with light, where nonequilibrium dynamics alter the primacy of one order over another. Following photoexcitation of La2−xBaxCuO4 (x = 0.115) with near-infrared pulses, we observe a long-lived state that exhibits enhanced superconducting correlations well above the equilibrium superconducting transition temperature. Our analysis reveals that this metastable phase arises from a collapse of stripe order, providing an important demonstration of light-directed control in quantum materials. Quantum materials are amenable to nonequilibrium manipulation with light, enabling modification and control of macroscopic properties. Light-based augmentation of superconductivity is particularly intriguing. Copper-oxide superconductors exhibit complex interplay between spin order, charge order, and superconductivity, offering the prospect of enhanced coherence by altering the balance between competing orders. We utilize terahertz time-domain spectroscopy to monitor the c-axis Josephson plasma resonance (JPR) in La2−xBaxCuO4 (x = 0.115) as a direct probe of superconductivity dynamics following excitation with near-infrared pulses. Starting from the superconducting state, c-axis polarized excitation with a fluence of 100 μJ/cm2 results in an increase of the far-infrared spectral weight by more than an order of magnitude as evidenced by a blueshift of the JPR, interpreted as resulting from nonthermal collapse of the charge order. The photoinduced signal persists well beyond our measurement window of 300 ps and exhibits signatures of spatial inhomogeneity. The electrodynamic response of this metastable state is consistent with enhanced superconducting fluctuations. Our results reveal that La2−xBaxCuO4 is highly sensitive to nonequilibrium excitation over a wide fluence range, providing an unambiguous example of photoinduced modification of order-parameter competition.